An Analysis of Post Attack Impacts and Effects of Learning Parameters on Vulnerability Assessment of Power Grid

Conference · Thu May 07 04:00:00 EDT 2020

DOI:https://doi.org/10.1109/ISGT45199.2020.9087639· OSTI ID:1669472

Paul, Shuva; Ni, Zhen; Ding, Fei

Due to the increasing number of heterogeneous devices connected to electric power grid, the attack surface increases the threat actors. Game theory and machine learning are being used to study the power system failures caused by external manipulation. Most of existing works in the literature focus on one-shot process of attacks and fail to show the dynamic evolution of the defense strategy. In this paper, we focus on an adversarial multistage sequential game between the adversaries of the smart electric power transmission and distribution system. We study the impact of exploration rate and convergence of the attack strategies (sequences of action that creates large scale blackout based on the system capacity) based on the reinforcement learning approach. We also illustrate how the learned attack actions disrupt the normal operation of the grid by creating transmission line outages, bus voltage violations, and generation loss. This simulation studies are conducted on IEEE 9 and 39 bus systems. The results show the improvement of the defense strategy through the learning process. The results also prove the feasibility of the learned attack actions by replicating the disturbances created in simulated power system.

• Research Organization: National Renewable Energy Laboratory (NREL), Golden, CO (United States)
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE)
• DOE Contract Number: AC36-08GO28308
• OSTI ID: 1669472
• Report Number(s): NREL/CP-5D00-77283; MainId:26229; UUID:ec6cec91-300f-40f0-a79a-30bac8718524; MainAdminID:13820
• Country of Publication: United States
• Language: English

Related Subjects

Markov decision process
POWER TRANSMISSION AND DISTRIBUTION
attack impacts
exploration rate
line outages
multistage sequential game
reinforcement learning
transmission and distribution system